Improving the similarity search between images of agricultural products: An approach based on fuzzy rough theory

Many methods have been presented in recent years for identifying the quality of agricultural products using machine vision that due to the huge amount of redundant information and noisy data of images of products, the retrieval accuracy and speed of such methods were not much acceptable. All of them try to provide approaches to extract efficient features and determine optimal methods to measure similarity between images. One of the basic problems of these methods is determination of desirable features of the user as well as using an appropriate similarity measure. This study tries to recognize the importance of each feature according to user’s opinion in every feedback stage through using weighted feature vector, rough theory and fuzzy logic for identifying important features and finding a higher accuracy in retrieval result. The proposed method is compared with fuzzy color histogram, combined approach and fuzzy neighborhood entropy characterized by color location. The simulation results indicate that the proposed method has higher applicability in image marketing compared to the existing methods.

A Comparison of Retrieval Result Relevance Judgments Between American and Chinese Users

Relevance judgment plays an extremely significant role in information retrieval. This study investigates the differences between American users and Chinese users in relevance judgment during the information retrieval process. 384 sets of relevance scores with 50 scores in each set were collected from 16 American users and 16 Chinese users as they judged retrieval records from two major search engines based on 24 predefined search tasks from 4 domain categories. Statistical analyses reveal that there are significant differences between American assessors and Chinese assessors in relevance judgments. Significant gender differences also appear within both the American and the Chinese assessor groups. The study also revealed significant interactions among cultures, genders, and subject categories. These findings can enhance the understanding of cultural impact on information retrieval and can assist in the design of effective cross-language information retrieval systems.

Research on Heterogeneous Digital Map Resource Query Algorithm Based on Web Services

Based on the current situation of digital map resource interconnection and mutual inspection, this paper studies three modes of unified retrieval of traditional guided data sources, and proposes a digital map resource retrieval model based on Web Services. This paper also designs digital map resources unified retrieval result fusion algorithm and metadata update algorithm based on Web Services in detail, which can be used for the development of digital map resource unified retrieval system.

Video Data Retrieval using Image Color Histogram Technique

In this paper, a subspace-based multimedia datamining framework is proposed for video semantic analysis; specifically Current content management systems support retrieval using low-level features, such as motion, color, and texture. The proposed frameworks achieves full automation via a knowledge-based video indexing and retrieve an appropriate result, and replace a presented object with the retrieval result in real time. Along with this indexing mechanism a histogrambased color descriptors also introduced to reliably capture and represent the color properties of multiple images. Including of this a classification approach is also carried out by the classified associations and by assigning, each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.

Content based video retrieval system using principal object analysis

Video retrieval is a searching problem on videos or clips based on the content of video clips which relates to the input image or video. Some recent approaches have been in challenging problem due to the diversity of video types, frame transitions and camera positions. Besides, that an appropriate measures is selected for the problem is a question. We propose a content based video retrieval system in some main steps resulting in a good performance. From a main video, we process extracting keyframes and principal objects using Segmentation of Aggregating Superpixels (SAS) algorithm. After that, Speeded Up Robust Features (SURF) are selected from those principal objects. Then, the model “Bag-of-words” in accompanied by SVM classification are applied to obtain the retrieval result. Our system is evaluated on over 300 videos in diversity from music, history, movie, sports, and natural scene to TV program show.

A new approach towards Image retrieval using texture statistical methods

Texture is a possession that represents the facade and arrangement of an image. Image textures are intricate ocular patterns serene of entities or regions with sub-patterns with the kind of brightness, color, outline, dimension, and etc.This article proposes a new method for texture characterization by using statistical methods (TCUSM). In this proposed method (TCUSM) the features are obtained from energy, entropy, contrast and homogeneity. In an image, each one pixel is enclosed by 8 nearest pixels. The confined in turn for a pixel can be extracted from a neighbourhood of 3x3 pixels, which represents the fewest absolute unit. We used four vector angles 0, 45, 90,135 to carry out the experimentation with the query image. A total of 16 texture values are calculated per unit. Compute the feature vectors for the query image by calculating texture unit and the resultant value is compared with the image database. The retrieval result shows that the performance using Canberra distance has achieved higher performance.

Improved Retrieval of Reflectivity Factor for Airborne Weather Radar

For the airborne weather radar signal processing, the intensity of the meteorological targets is represented by the physical quantity called reflectivity factor. When observing the middle-or long-range meteorological targets at low altitude from a high-altitude radar platform, due to the incomplete beam filling, the retrieval result of the reflectivity factor is much less than the actual value. In this paper, we firstly introduce a beam filling coefficient into the weather radar equation and then present its estimation method based on the real-time radar operating parameters. The validity of the proposed method is then verified by processing the raw-data of the simulated meteorological targets.

Image Characteristics Indexing Based on X-Tree

Image data set are usually very large, which might consist of millions of image objects, it is essential to use an efficient and effective indexing technique to facilitate speedy searching. The features can be expressed in terms of high-dimensional vector data which can be compared with a given query for similarity between them. It is more important that the image database should be preprocessed and establish indexing to improve retrieval efficiency. In this paper, the method of improved X-tree is proposed, design and implementation of a high dimensional index application to facilitate the speedy searching in feature based image information retrieval. Compared by retrieval efficiency and retrieval result, it is convincingly proved that hierarchical index structure based on clustering is efficient and applicable in image characteristics indexing.